{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>oni</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>4</td>\n",
       "      <td>-1.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>5</td>\n",
       "      <td>-1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>840 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  month   oni\n",
       "0    1950.0      1 -1.53\n",
       "1    1950.0      2 -1.34\n",
       "2    1950.0      3 -1.16\n",
       "3    1950.0      4 -1.18\n",
       "4    1950.0      5 -1.07\n",
       "..      ...    ...   ...\n",
       "835  2019.0      8  0.11\n",
       "836  2019.0      9  0.13\n",
       "837  2019.0     10  0.29\n",
       "838  2019.0     11  0.46\n",
       "839  2019.0     12  0.55\n",
       "\n",
       "[840 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = np.arange(13)\n",
    "data = pd.DataFrame(np.loadtxt(\"/home/satyam/Documents/Thesis_Work/Data1/oni.txt\"),columns=col).set_index(0).stack()\n",
    "data = pd.DataFrame(data).rename(columns = {0:\"oni\"})\n",
    "data = data.reset_index().rename(columns = {0:\"year\",\"level_1\":\"month\"})\n",
    "data = data[data[\"year\"] != 2020]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X2MjYESNgq61CR1M4QlQPgZXsDj4YLrww/JrVDRvCxRfbIMh588LGEmOhSwRbJCLdRWSKiExZ7DNrZt8XX8CoUdY+kJjYzlXe//yPTTexaFH2712jhnUt3nZb+MMfwjceX3ONdXC4886wccRYzicCVR2iqu1UtV0jnzIh+xKD7tq2DRtHoUj0lunWDTZtgiefDBNHkyZWIpk3L3PjTcqqeXM47zzr2FDeZVOHDoXDDsutxu88lPOJwLmC8vDD9sF18cW2Qlyo6iGwNh+waTBCu+46mxrln/8s33nTp8Nbb8Fzz2UmrpjwROBcNu24I0yaZK/POgvee8/rxgF2390Wqpk6tWLnZ2LwZIyE7j46GngP2F1EFojIRSHjcXngp58KZwrnM8+07eOPh40jVyS61lbEpEkVTyIueK+hs1S1qapWV9Vmqpo7k+ivX2+9ZFxu2W67wum51KoV/P73YauHCkHNmlC7duGUCq66Clq3zuott5gIRORIEblCRHqIyJHZCConXHmlNWB98EHoSFxJCuXp76yzrJ7bB1RVXK1a0LWr9W778cfQ0VTe4MHw9ddF32/aBKedZm0oGVJqIhCRHUXkA6AP0BrYBegjIpNFZMeMRZQNiWmB16wp/ZhEt74rr/R55HNJ9eq27dIFVq6E00+3mSzztddIly4W/5gxoSPJb1deadOuF9I6HI0a2eR8Vava70ffvhm7VaoSwb3AA6p6uKperap/VdXDo/33ZyyibNh1V9um6mnQqZNtJ0+2hqxzzoGZMzMfm0utUSObWXXuXLj00qKlH0N3gayoxo3tifbnn0NHkt92391m3S2EdTgOOsi2Z5wBu+xStL9584zdMlUi2FNVHy2+U1X/DbTJWETZkHiqBFi6tORj6ta17euvw9VXw9ixsPfe1v3PhXXYYXDrrVYVkJjX/swzbXSqi6+ePa0kH2psRrrstpu1Hz3wgJUE3nvP9rdqlbFbpkoEJTbhi0iV0t7LS1vqsdG0qc29/803NifKpz4/Xk7o3RuOPrpoVOyhh1qbzr/+FTYuF86xx1rJ4P/+z6dJL6dUieB5EfmXiNRO7IhePwiMy3hk2TJ8eNmO2267zUsSLqyqVTfv1fXii1Y10L174fQeqawRI2DGjNBRVNyKFdC/f9mPr1LF2gqmTIEPP8xcXAUoVSK4DvgZ+EZEPhKRj4B5wAqgVxZiy7yDD7ZfGO+xkZ+aNCnqe169uhWjTz3Vut+99FLY2HLBeefZ6OV8df75cOON1j5XWhVucUcdZdu5czMXVwEqNRGo6npV7QU0B7oBFwAtVbWXquZ5a0zk7LOhWrWylwpc7qlWreh1zZq2FjXYZHkuvx14oG1HjYKWLaFXL1uLO5VEm1EIK1bA5ZfDO++Ei6GCUnUfbS8iTVR1jarOAPYDRovIPSLSIHshZtD221vvoBEj8rf7YQjTpuVulUOVEn6lhw2DU06B117zuuN80rixbceNs1lS777bGkxztavt++9bA+8hh9j4kG+/DR1RmaWqGnoI+BVARA4DBgD/xqqLhmQ+tCzp1g2+/95W4MqGRDfBHj2KvkRgp52yc/90+OUX650xfXroSMrm5Zetq/DRR9v0zy+84Akhn+y1lz2szZljD2y5vsbx6afbqmtt2sBXXxXN4JvDUvYaUtXEROV/Aoao6tOqejM2uKwwnHgiNGgAjz6anfslio0ffghPPGFfkJ8Tj51wAnz3XegoyqZ1a3taW7QITjrJlkj09S3yy847V24+omy5+mqYNct+zyDcSnTlkDIRiEiiAvZo4LWk96qVcHx+qlnTinHPPpudQT2JJ9H77rMPosWLbaDIBRdk/t7p9vPPlkhXrgwdyZbVqGED0ObMgX79bIoKHyDoMqVly/QtBZqots5g9XWqRDAamCQiY4E1wFsAIrILVj1UOLp1sxktE0/nrmyeesrGVXTpkhtz2pdF9epWh+tcttSpU7nzE11hJ0yofCylSNVr6DbgGmyB+UNU/1upWgX4S8YiCuGAA2w037PPho4kvxx3nFW3vPyyrWvsnNucal6UmFNW8ajq+yXsm5O5cAIRsR4KqSahcyW7+GIrFdxzD9x0U0bnQ3HOZUapiUBEVgKJUkCic65G59RQ1cJpJ3CV0769bTM4Ta5zLnNK/TBX1brJ34tIXeBy4BIgRzvyOuecK68tPtWLyDbAVcD5wCigvaqWcby3cy4tli/fvK55/vzKN0I6F0lVNdQQayz+EzAUaKuqhdVbKBUfcORyxZIltuh9vs+z70r25ps2riVgYk9VIvgGWAwMA34BLpKkeTxU9a7MhhbYpEm2YEiTJum97u6723aXwhmT5zJs+XJLAhddZGscA/z5z2FjyrTFi+GVV0JHkXmrVsHhh1s7W8AR06kSwR0UNRbXTXFc4Vm+3Ia0n3021K+f3mv37AkdOhT9QTtXVkccAeeea68LMRF89JFtmzXbfMT6okXQokWYmDJt7VrbfvVV0DBSNRb3yWIcueXf/7b5QXr0yMz18z0J3HuvLdLjXCYccQS0bQt9+tgTcy5UiW3YYIMRJ0yAjh1DR5N2qWYffSLp9cBi7xVumU3V1j098ECrt3O/1aNHfk6J4fLDiBFwzTWw776hIymSWJmwV2EsxVJcqikmdk16fUyx9xplIJbc8OqrMHt25koDZfHrr7+dhO7rr/NnGgfnXF5JlQhSdZsp3C41990HDRvCH/8YLoajjrJpqRMDtH7+2WbPPPXUcDE55wpWqsbirUWkLZYstopeS/S1VTaCy7pvv7Un7+uusx5DoSTm+V+3zmZHTcyKmi/z/zvn8kqqRLAQSHQRXZT0OvF94Zk71+YduuSS0JE451zWpOo1dGQ2AwkuUQ3TubMth+fyw003wc03W8kpTp5/3ud2cmmTqo0gXhKDOUI2Ervyu+km6+kVctHyEDp3tiURC1mNGptvXcZ4IijumOIdpJwLbNOm0BGEMXQoHHSQDcB0GeWJoLgq/iNxOSax5GGbNmHjyLZWreC990JHEQupJp1LOZpKVT9OfzjOuc388AMMGmRdh9u1Cx2NCyGx3kcGl1hN1WvoHyneU+CoNMfinCvu1ltt5bwBA8p33uzZRa+ff/63+1z+OPjgjM+GHLTXkIgcDwwGqgIPq2o5f9sL3JFH2sCyxIjiJUsyf8/TToMxY3wa7lwwezYMGWLdmXfbrXznDhpU9Prkk4te16uXnthcQUlVNXRYivNUVd+qzI1FpCpwHzZ9xQLgQxF5TlU/q8x1C8Jpp8Hw4TaobeZMmDXL9mdjTeUxBbL43IoV9m8ZN86mM95xx9ARlV/v3rDVVnDLLeU7b/58mzgxYcqUotdNm8avh5XbolRVQ9eWsE+BfYFm2FN8ZXQAvlTVrwBE5HHgFMATQWLNgtdes/7xM2bAPvuEjSlfbNxo25tvtm29enDssdC9e9ExidJOLpd63nnHElm/frD99uU79x9Rre68edCyZdpDc4UnVdXQScnfi8ghwI3YiOMr0nDvHYH5Sd8vAA4sfpCIdAe6A7Qo1DnJXfr8nLSI3htvwP/8j00fnOyNN2x7221W/ZZrVOHaa2GHHeDqq8t37uLF8K9/2VoangRcGW2xr6SIHC0ibwD9gLtU9SBVfT4N9y6pfPqbRzRVHaKq7VS1XaNGBTrp6fz5MGyYrYpWViJexE9l551t5afiSQCKSgKvvlrye7Nnw/vvZza+VJ591rpN9u1b/nUf7rnHqhD/9rfMxOYKUqo2ghOxEsDPwI2q+k6a770AaJ70fTPg+zTfI78ccQSccQZst13oSApb8liRadNs3vuPPoJHH7U2hcRqUe3bh2lcffttax/q1q385z7/vHU13WOPtIdVKdOnwxdfFP5o6DyVqo3geezDeilwvRR7+lTVk0s6qRw+BHYVkZ2A74AzgbMrec381revdRP85ZfQkRS2yy6zbplgixA99BBceCHMmWOrT/XqBZ06hZ1zqlo1qFrBZrjevdMbSzokFpnJ5XaZGEuVCDJaeaqqG0TkCmA81vA8VFVnZvKeKeXCBF4332wrf/XubU+lJVVruMpr3Ng+kC66yFbDGjjQFgM6+eSiUbz5JrH2bcOGRQOQnCujVI3FkwBEpBawC1Z/P1dV16br5qo6DhiXrutVSq5MbNWsGTz2WOgo4qFHD5vPZvjw0JFU3muv2ba8jcvOkXrN4moiMgirHhoOjADmi8ggEfFH1WxKrtPOhYW8C8X++9ukZvffX7GJ3fbaK3ca7BMl2nwZL5FIXN4elhNS9Rq6A2gA7KSqB6hqW2BnYBvgzmwE5yJ16xa9PuIIWLAgWCgF5/LLrW1gzpzyn/uZD3mpkDlzoE8f+MMf4MDf9Bh3AaRKBJ2Bi1V1ZWKHqq4ALgNOyHRgrhQzZkDbtqGjKBx//KPVq7vs2LQJ/vxn6xV1//25U6Iqq+TR/fvtl3/xlyLl4vWqv23iV9WNFPLi9bmseXObLqBx49CRFI5ateyDCWzNapdZDz4Ib70Fd91l013ki8To7jlzYPVqez1tWrh40ixVIvhMRM4vvlNEzgVmZS6kArJpkw0WS2dD9O67wwcfpO96rmiN6nQMInvoofxeSObTT+HNNzN3/euvt8WfKjJGoqKOO84GF1ZGcjvd3/9euWvloFTdR3sAz4jIhcBHWCmgPbAVcGoWYst/o0bZQJphw9J73dq14ayzNp9MzFVcusYLvPACXHqpteH065eea5ZVYsxBRcceJPzud7bNVH9/VZtRtaQqlfbtMzN245VX0nu9O++E884r+n78eEs2eazUEoGqfqeqBwJ9gXnAt0BfVe2gqt9lKb78tWqVPf20awfn/6ZgtblzzrFujKVJLMyeiVGuVatau4MP9Km8RYtsu3Bh9u89YIC1dfzpT9m/d3ncfnvpH/aTJ8MTT2Q1nHLr29c6b1x+uc1jBTb+ZOzYil0vsUZEgwbpia+CUpUIAFDV14DXshBLYbn9dvj+e3jqqS0vfzliROr3GzeG666DK9Ix118SERu89ve/2yRnd9yR3uu77Gnc2Cacy1VVq9rMsD16hI6kcho1sgGI3btDnTrWbte0qU2dUZHqrsQD4GmnpTXM8tpiInAV8NVXNhXwuefC73+fnmsOHJie6xTXty8sX27x+qIl6eGlq986/3yYOLEw1gS/6CKr7n3vPWjdGiZMgM6d4ZFHynedtWth5Eh7HbhEUAD/K2WwZInV1WfLtdfaE1B5lxcMQQQGD7anmfIugJKJWPK5O16TJrYdOrRoXQSXn0aPLr1XU5Uq8MADRe0x9erByy9bIziUvXPImDG5MbUNcSkRJKavzsaT2muvwTPPWHVLNkZ5rllj01cnr0hVXlWq2Bz2q1ZZVZarmB12KHp9ww2ZK8W5zDt7C/Nf7ruvLQe6MhpmtfXW8NxzVjoo6zifoUOtW+qPP1Yu1jSIRyLIlsWL4eij7XU25nyZN8+G6K9ZY0sannJKxQebVatmxdR8TwSJp7FatcLFsMMO9iGx//7hYnCZV/xvvFYtOOmkko8tbt48qyq7/vqceGCIR9VQtixdatvjjrMP5kzq0AF22QUuvtiKpT/9ZAuaVKauMblImxg0k29atLCpC555JlwMgwfDwQfb1NY+SM2VZNgwqwbN5niKFLxEkE6J1aS6dMn8va66yr4y5d577WklH40ZE/b+NWpYyapdO/jOe1q7EgwbZmtp58jyu14icCUbOHDz9X9d+TRpYqWSGjWs2i3fNG9uA7xuuKFi5y9cCOvXpzemQrJsmfU+yhGeCFzJli2z+WDiJDGqNl06dLB64L5903vdTNp1V9t27GgD1NatgzZtbA3oslq3ztpJOnbMTIyFoGZNG4iWI/LwUcVlxRlnWCI49NBwMYwdax+i48dnfobQTPUoC/nzq4h99rFSTGWmRUl0ifzkk/TEVEiWLLHtuedaMsiRZWk9EbjNrV5tX0uWWNVGqB4N//mPTb2xcaM1uPpU0a4QPPusbS+4IGwcxXjVUJxs2LDlY7be2sZd7LGHPbVMnJj5uIp79FHrx73NNtm/t3OZtHy5bctT1ZYFnggq4913bbbJfLBpkxX3W7Uq+/KAffpkMqLSXXCBjce4994w93/7TWoAABBjSURBVHcuZjwRVMbBB9v88/lg5EiYOhX69y/7VMU77VQ0w+K8eRkL7TdOOslGaSa64+aiFStCR+Bc2ngbQXn8/LMNFqpTJ7+Wi1yzBm680fq1l3ea4gsvtJJPNhv+nn4aqlfP3v0q4rnnQkfgXNp4IqiIAQPya1Kxf/7TVkr797/LP/tjNj+Qjz/eRkjnehKAonnzExPNudJ9+qmtNVDZRXNcxnjVUEWsXGm/2IGnji2TpUutOqhzZzjiiHBx5PPyjcUtX25dWqEwplXOlJUrbf2Bffe1tTnyYTbemPLf4orYaisbddm9e3rXI86EN96wP8hsdwMVKVoLeN06q+7Zc8/sxpApzz0Hv/6a/gFoheihh+Cyy+CLL8KvnpZYQc79hieCQjZ3rm0vuqjiH8KJtpDErKrlMWSIbe+7zxqbb7+9YjHkmieesDliOnQIHUluSp5SY+pU6/1V1p5qmbTXXvD442EXDkqsbVy/frgYSuBtBIVs7Vrb3nprxa/xu99V7g9n2TJbm+G444oW7shny5bZYug9exb1CXebS7QF9O8Pe++dufusXWv/H1ty1FG2MFXr1nDWWTZrbyjDh9tXjolXieDMM62K5K23QkeSXaWttJQN/fvbB+agQeFiSKexY20ytWzMMOtK9vTT9oBSp451gCjLwMM2beCdd6ydIjE1eD6vhpdm8SgRtG8PH34IH3xgUxck5EPvlHx3zz3QtavNYVMI5s61QXnt2hVVfbnsadPGFoDaddeihZgOOaRs51arZlOrd+4ML75oo+fLKtF9ukCnOolPIvj6a/v66Sert/zmG5sPvJA9/DA0axY2hipVoF+/sDGkW5cu/jQZytSplf/Z77WXfZXH7bfb1Nw5NGNoOsUjESRr0MDqDOMgF+Y7/+tfwyejdPNqoXBCJOC337bq5MGDM99LcMMG61iR5X9nvNoIXPbl6ypnqRTqWsSqPnVGSW6/3aqE/vznzN5n/XqbcffNN+HyyzN7r2I8EZTXZZeFjiB/NGuWc93kKix5JHkhVgutWmUzvk6YAIcfHjqa3DFtGowbZ73EMjn31dq1cPrp1jX5zjuhV6/M3asEQaqGROSPQB9gD6CDqk4JEUe5hex/nG/q1CmsKpTE/33NmmHjyITPPrN2tDlzrJdXIZbiKmrAAPtd7tEjs/cZONAWqbnvvqyXBiBcieBT4DTgzUD3d84l3H+/daKYOBF69y7/tBmrV4dZtyIbnnjCPpi33Taz91m71qaJD5AEIFCJQFU/B5BCLGI7ly9Wrix6/cknts5wRXTqZI2pS5fmx/xb5VG9unV4yJSttrIquVNPteVhA8n5XkMi0h3oDtCiRYvA0bjYSow5KaQux4mn3CuvrHgSgKK1KlatKrxEcMEFmZ1hVsTWCgksY4lARCYCJf0Eb1TVsWW9jqoOAYYAtGvXzivpXRhVq265jSj5CTsfbLutt3uVpnp168Vz7bWhI8mKjCUCVe2YqWs7l3NmzbKufw0aFG730ji5/HIbN9C6dehIssK7jzpXWTNmWJfL9ett2u9CG0AXR/XqFWY34VIESQQicqqILAB+D7woIuNDxJF2XsyOn+XLbcGf6tVtIJCvUeDyUKheQ2OAMSHunVHTp9u2Zcuwcbjs+eUX2H57ePXV2FQjuMLjVUPpNG6cDT4p62yIhSIx/3xZ5oYvJHvsYcswvvmmJwGX1zwRpIuqTW3bsWNhjj5NpWNHG34/eHDoSLLr6qttNszmzUNH4lyleCJIl5kzYf58OOGE0JFkX6NGNgR/9GibpsA5l1c8EaTLuHG27dQpbByh9OoFtWrZspTOubziiSBdxo2z+uK4dh3cfnubmXXkSPjii9DROOfKwRNBOvz8sy1eEcdqoWTXXmvtI7fdFjoS51w5eCJIhwkTbL76uCeCxo3h0kthxAibd8Y5lxc8EaTDuHGwzTZw0EGhIwnvuuuKJmhzzuUFTwSVtWkTvPQSHHccVMv5yVwzr0kTuOQSe/3VV2Fjcc6ViSeCytqwARYt8mqhZIkVrp59NmwczlVU8tKkMeCJIF2OPz50BLmjaVPbVmaOe+dC2bTJHmJiNG+U12WkQ/v21n3SFVm2DGrXDh2Fc+X3/PO2jnMOLBiTLZ4IKmPJEtueeGLYOHLRNtuEjsC58lOF/v1t7qguXUJHkzWeCCrjlVds6+0DzhWG11+HyZPhgQdi1fnD2wgq49tvbXvAAWHjcM6lx+23W8+3bt1CR5JV8Ul5mVTF86lzeW/KFCsNDBpk82bFiH+COecqp1BW5ps82dq2Lr00dCRZF48SQadO0KJF6ChcZa1da18uvBkzoGtX+OQTWLDA9uVznXqi48cVV0DdumFjCSCP/+fKoXNn+3L5a/FiOOUU+OknOOaY0NHEW4MG8MEH9uHZti18+SWsWZNb3YUXLYL33oOFC+H776Fhw7Kd17NnZuPKUfFIBC6/zZ5tXfm++w6efBLOOCN0RPE2fryt1ZwYOHj33bZaWy5IlEqKx3PeeWU7v6wJo8B4InC57/zzYdttrWufT+wXXv369pWLWreGYcOs3aJJE0tWTZr4gM8t8ETgcpeIbXfe2WZ49QXic1Pi/+nQQ2HHHW3p0pCxxKzrZzp4InC569BDoV8/uPxyq5d2uenUU2HWLKu6W7TIGpK32gp22y10ZK6MPBFUxsyZ8OKLoaMoXNtsAzfdFDqK7FK1eW7yqR97y5bw4IOho3CV4ImgMvbc076cS5cnn4SXX7ZBTS57dt45luMHEjwROJcrli61fuwHHAB//WvoaOLlyy9DRxCUJwLncsVVV9n03RMn5vfgLJd3/LfNuVwwYQJMnQr/+7+wzz6ho3Ex43MNOZcLpk619qYbbggdiYshTwQud8R5HqEqVWDoUKhZM3QkLoY8EbjcMH06TJoEhx8eOpLs22UXKwkceGDoSFxMeRuByw3XX2/jBnr3Dh1J9k2bVjQ617kAPBG48CZOtL7zd95pcwrFjScBF1iQqiERuUNEZonIdBEZIyK+0nlcbdoE114LrVpZH3rnXNaFaiOYAOytqvsAc4AY1gc4AEaOtB4zt93mDaXOBRIkEajqK6q6Ifr2faBZiDhcDnj1Vdh/fzjzzNCROBdbudBr6ELgpdBBuIDuvNO6TzrngshYY7GITASalPDWjao6NjrmRmADMDLFdboD3QFa+LrDheeEE+DII0NH4VysZSwRqGrHVO+LSFegM3C0qmqK6wwBhgC0a9eu1ONcnmnRAnr1srUGnHNBBek+KiLHA9cDh6vqLyFicIFVrQp33BE6Cucc4doI7gXqAhNEZKqI+KoWzjkXSJASgaruEuK+zjnnfsu7ajjnXMx5InDOuZjzROCcczHnicA552LOE4FzzsWcJwLnnIs5STGoN+eIyGLgmwqe3hBYksZwMs3jzbx8i9njzaxCjrelqjYq7c28SgSVISJTVLVd6DjKyuPNvHyL2ePNrDjH61VDzjkXc54InHMu5uKUCIaEDqCcPN7My7eYPd7Mim28sWkjcM45V7I4lQicc86VwBOBc87FXF4nAhEZKiI/isinSfv2FZH3RGSGiDwvIvWi/a1EZE20/sFmayCIyAHR8V+KyD0iIqHjjd7bJ3pvZvR+rVyNV0TOSfrZThWRTSKyXw7HW11Ehkf7PxeR3knn5GK8NURkWLR/mogcESDe5iLyevTzmikiPaP9DURkgoh8EW23TTqndxTXbBE5LpsxlzdeEdkuOn6ViNxb7Fq5GO8xIvJRFNdHInJUheNV1bz9Ag4D9gc+Tdr3IbbyGcCFQL/odavk44pdZzLwe0CAl4BOORBvNWA6sG/0/XZA1VyNt9h5vwO+yvGf79nA49HrrYF5QKscjrcHMCx6vT3wEVAly/E2BfaPXtcF5gB7AoOAv0X7/wYMjF7vCUwDagI7AXOz+TtcgXhrA4cAlwL3FrtWLsbbFtgher038F1F4037L0u2vyj2AQ+soKgRvDnwWUnHFfvhz0r6/izgoRyI9wRgRL7EW+yc/sBtuRxvFMfzWMLdLvqja5DD8d4HnJt03KtAh2zHWyz2scAxwGygadL/9+zodW+gd9Lx46MPpyAxbynepOO6kZQIcj3eaL8AS7GkW+5487pqqBSfAidHr/+I/TEl7CQin4jIJBE5NNq3I7Ag6ZgF0b5sKS3e3QAVkfEi8rGIXBftz9V4k/0JGB29ztV4nwJWAwuBb4E7VfUncjfeacApIlJNRHYCDojeCxKviLTCnkg/ABqr6kKAaLt9dNiOwPwSYst6zGWMtzT5EO/pwCequq4i8RZiIrgQ6CEiH2HFq1+j/QuBFqraFrgaGBXVv5ZUd5bNPrWlxVsNK6aeE21PFZGjyd14ARCRA4FfVDVR752r8XYANgI7YNUW14hIa3I33qHYH/QU4P+Ad4ENBIhXROoATwNXqeqKVIeWsE9T7M+IcsRb6iVK2Jcz8YrIXsBA4JLErhIOSxlvkDWLM0lVZwHHAojIbsCJ0f51wLro9UciMhd76l4ANEu6RDPg+9DxRnFNUtUl0XvjsPrkETkab8KZFJUGIHd/vmcDL6vqeuBHEXkHaAe8lYvxquoG4K+J40TkXeALYFk24xWR6tiH1EhVfSba/YOINFXVhSLSFPgx2r+AzUuMidiy9jtRznhLk7PxikgzYAxwvqrOrWi8BVciEJHto20V4Cbgwej7RiJSNXrdGtgVa9BcCKwUkYOilvXzsbq5oPFi9an7iMjWIlINOByrL87VeBP7/gg8ntiXw/F+CxwlpjZwEFavmpPxRr8HtaPXxwAbVDWrvw/R9R8BPlfVu5Leeg7oGr3umnT/54AzRaRmVJ21KzA5WzFXIN4S5Wq8IrIN8CLWDvNOpeLNdINHhhtTRmNVPuuxLHgR0BNr+JsDDKCo4e10YCZW1/oxcFLSddphdbNzgXsT54SMNzr+3CjmT4FBeRDvEcD7JVwn5+IF6gBPRj/fz4BrczzeVlij4efARGxa4WzHewhWxTAdmBp9nYA1tr+KlVBeBRoknXNjFNdsknquZCPmCsY7D/gJWBX9n+yZq/FiDwqrk46dCmxfkXh9ignnnIu5gqsacs45Vz6eCJxzLuY8ETjnXMx5InDOuZjzROCcczHnicC5FKJxBm+LSKekfV1E5OWQcTmXTt591LktEJG9sTEHbYGqWH/t47VoJGd5rlVVVTemOUTnKsUTgXNlICKDsME7taNtS2y67WpAH1UdG00U9lh0DMAVqvqu2NoBt2CDx/ZT1T2zG71zqXkicK4MoukdPsYmgXsBmKmqI6Jh/pOx0oICm1R1rYjsCoxW1XZRIngR2FtVvw7zL3CudAU36ZxzmaCqq0XkP9jUA12Ak0SkV/R2LaAFNrHXvWIrs23EJjVMmOxJwOUqTwTOld2m6EuA01V1dvKbItIH+AHYF+uIsTbp7dVZitG5cvNeQ86V33jgL4l1YEWkbbS/PrBQVTcB52ENy87lPE8EzpVfP6A6MF1s4fl+0f77ga4i8j5WLeSlAJcXvLHYOedizksEzjkXc54InHMu5jwROOdczHkicM65mPNE4JxzMeeJwDnnYs4TgXPOxdz/A6tmysRvsmPjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " plt.plot(data['year'],data['oni'], 'r') # 'r' is the color red\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('ONI ENSO')\n",
    "plt.title('Time Series plot of ONI')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
